
> # load data
> df1 <- read_excel("dat/auxiliary/tenant_total.xlsx", sheet = 3)

> df2 <- read_excel("dat/auxiliary/tenant_above.xlsx", sheet = 3)

> df3 <- read_excel("dat/auxiliary/tenant_below.xlsx", sheet = 3)

> df1 <- df1[11:52, 1:2]

> names(df1) <- c("geo", "total")

> df2 <- df2[11:52, 2]

> names(df2) <- c("above")

> df3 <- df3[11:52, 2]

> names(df3) <- c("below")

> df <- cbind(df1, df2, df3)

> df <- df %>% dplyr::filter(
+   geo %in% c(
+     "European Union - 27 countries (from 2020)",
+     "Belgium",
+     "Bulgaria",
+     "Denmark",
+     "Germany (until 1990 former territory of the FRG)",
+     "Estonia",
+     "Ireland",
+     "Greece",
+     "Spain",
+     "France",
+     "Croatia",
+     "Italy",
+     "Netherlands",
+     "Austria",
+     "Poland",
+     "Portugal",
+     "Finland",
+     "Sweden",
+     "Norway",
+     "Switzerland"
+   )
+ )

> df$geo[df$geo == "European Union - 27 countries (from 2020)"] <- "EU-27"

> df$geo[df$geo == "Germany (until 1990 former territory of the FRG)"] <- "Germany"

> df_long <- pivot_longer(df, total:below)

> df_long$value <- as.numeric(df_long$value)

> df_long$geo <- reorder(df_long$geo, df_long$value)

> ggplot(df_long, aes(x = geo, y = value, fill = name)) +
+   geom_bar(position = "dodge", stat = "identity") +
+   xlab("") +
+   ylab("Tenants in Rented Property \n at Market or Reduced Price (in % of Population)") +
+   coord_flip() +
+   scale_fill_manual(
+     labels = c("Above Poverty Threshold", "Below Poverty Threshold", "Total"),
+     values = c("antiquewhite4", "darkgoldenrod1", "steelblue4")
+   ) +
+   theme(legend.title = element_blank(), text = element_text(size = 16))

> ggsave("fig/share_tenant.pdf")
